Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds

A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.Comment: To be published in proceedings of 3DIMPVT 201

Ramadan Fasting During the COVID-19 Pandemic; Observance of Health, Nutrition and Exercise Criteria for Improving the Immune System

Fasting is one of the religious rituals of Muslims worldwide who refrain from eating foods and liquids every year during Ramadan. This year (2020), Ramadan is very different from previous years due to the outbreak of a terrible microscopic giant called coronavirus disease 2019 (COVID-19). The pandemic COVID-19 has made Ramadan very important this year because the virus has infected millions of people around the world and killed thousands, especially people with immunodeficiency. In dealing with COVID-19, maintaining good hygiene and supporting the immune system are effective, preventive approaches. Moderate exercise training and proper nutrition are the most important factors to support immune function. Lack of facilities, poor health and many traditions that lead to public community gatherings have made many Islamic countries susceptible to this dangerous virus. In such an unprecedented situation, there are many Muslims who doubt whether they can fast or not. Therefore, the proposal of usable exercise programs and effective nutritional strategies is imperative. In this study, we will look at the proposed health effects of fasting and its impact on the immune system, the effects of Ramadan intermittent fasting on resting values and responses of immunological/antioxidant biomarkers in elite and recreational athletes, together with the important health, nutrition, and exercise advice that fasting people need to follow in the event of a COVID-19 outbreak. © Copyright © 2021 Moghadam, Taati, Paydar Ardakani and Suzuki

Pain Detection in Masked Faces during Procedural Sedation

Pain monitoring is essential to the quality of care for patients undergoing a medical procedure with sedation. An automated mechanism for detecting pain could improve sedation dose titration. Previous studies on facial pain detection have shown the viability of computer vision methods in detecting pain in unoccluded faces. However, the faces of patients undergoing procedures are often partially occluded by medical devices and face masks. A previous preliminary study on pain detection on artificially occluded faces has shown a feasible approach to detect pain from a narrow band around the eyes. This study has collected video data from masked faces of 14 patients undergoing procedures in an interventional radiology department and has trained a deep learning model using this dataset. The model was able to detect expressions of pain accurately and, after causal temporal smoothing, achieved an average precision (AP) of 0.72 and an area under the receiver operating characteristic curve (AVC) of 0.82. These results outperform baseline models and show viability of computer vision approaches for pain detection of masked faces during procedural sedation. Cross-dataset performance is also examined when a model is trained on a publicly available dataset and tested on the sedation videos. The ways in which pain expressions differ in the two datasets are qualitatively examined.</p

Distribution and expression of virulence genes (hlyA, sat) and genotyping of Escherichia coli O25b/ST131 by multi-locus variable number tandem repeat analysis in Tehran, Iran

Escherichia coli ST131 is a pandemic clone with high antibiotic resistance, and it is a major causative agent of urinary tract infection (UTI) and bloodstream infections. This study evaluated the distribution and expression of virulence genes and genotyping of E. coli O25b/ST131 by Multi-locus variable number tandem repeat analysis (MLVA) method among UTI in patients at Tehran hospitals, Iran. A total of 107 E. coli isolates were collected from UTI patients. Polymerase chain reaction (PCR) amplification of the pabB gene was used to identify E. coli O25b/ST131 and the prevalence of sat and hlyA virulence genes was also analyzed. The microtiter method quantified biofilm formation ability in E. coli O25b/ST131. The Real-Time PCR (qRT-PCR) was performed to evaluate the expression of sat and hlyA genes. Finally, MLVA was performed for E. coli O25b/ST131 genotyping by targeting seven tandem repeats. SPSS-16 software was used for statistical analysis. Molecular study showed that 71 of isolates carried the pabB gene and were considered E. coli O25b/ST131 strains. Also, 45.8 and 17.8 of isolates carried sat and hlyA genes, respectively. The 57.9 isolates had biofilm formation ability. Expression of the studied virulence genes showed an increase in strong biofilm producing E. coli O25b/ST131 strains. A total of 76 (100) E. coli O25b/ST131 strains were typed by the MLVA method. High prevalence of E. coli O25b/ST131 isolates in UTI patients can be a serious warning to the treatment due to the high antibiotic resistance rate, expression of virulence genes, and biofilm formation

Chasing feet in the wild: A proposed egocentric motion-aware gait assessment tool

Despite advances in gait analysis tools, including optical motion capture and wireless electrophysiology, our understanding of human mobility is largely limited to controlled conditions in a clinic and/or laboratory. In order to examine human mobility under natural conditions, or the 'wild', this paper presents a novel markerless model to obtain gait patterns by localizing feet in the egocentric video data. Based on a belt-mounted camera feed, the proposed hybrid FootChaser model consists of: 1) the FootRegionProposer, a ConvNet that proposes regions with high probability of containing feet in RGB frames (global appearance of feet), and 2) LocomoNet, which is sensitive to the periodic gait patterns, and further examines the temporal content in the stacks of optical low corresponding to the proposed region. The LocomoNet signicantly boosted the overall model's result by ltering out the false positives proposed by the FootRegionProposer. This work advances our long-term objective to develop novel markerless models to extract spatiotemporal gait parameters, particularly step width, to complement existing inertial measurement unit (IMU) based methods